Capture Plant Research Articles

Optimum Conditions and Maximum Capacity of Amine-Based CO2 Capture Plant at Technology Centre Mongstad

Optimum Conditions and Maximum Capacity of Amine-Based CO2 Capture Plant at Technology Centre Mongstad

Artificial intelligence‐driven sustainability: Enhancing carbon capture for sustainable development goals– A review

AbstractArtificial intelligence (AI) and environmental points are equally important components within the response to local weather change. Therefore, based on the efforts of reducing carbon emissions more efficiently and effectively, this study tries to focus on AI integration with carbon capture technology. The urgency of tackling climate change means we need more advanced carbon capture, and this is an area where AI can make a huge impact in how these technologies are operated and managed. It will minimize manufacturing emissions and improve both resource efficiency as well as our planet's environmental footprint by turning waste into something of value again. Artificial intelligence could be leveraged to analyze huge data sets from carbon capture plants, searching for optimal system settings and more efficient ways of identifying patterns in the available information at a larger scale than currently possible. In addition, AI incorporated sensors and monitoring mechanisms in the supply chain can identify any operational failure at reception itself allowing for timely action to protect those areas. Artificial intelligence also helps generative design for carbon capture materials, which allows researchers to explore new types of carbon‐absorbing material, including metal–organic frameworks and polymeric materials that are important in industrial CO2, such as moisture. In addition, it increases the accuracy of reservoir simulations and controls CO2 injection systems for storage or enhanced oil recovery. Through applying AI algorithms on reservoir geology, production performance and real‐time data this study would like to facilitate the optimization of injection processes as well as minimize CO2 emissions while assuring a maximum efficiency. Artificial intelligence integrates with renewable‐based carbon capture efforts that can be employed by AI‐driven smart grid systems to improve carbon capture methods.

A method for siting adsorption-based direct air carbon capture and storage plants for maximum CO2 removal

Adsorption-based direct air carbon capture and storage (DACCS) is an emerging approach to mitigate climate change by removing CO2 from the atmosphere. Recent studies show separately that thermodynamic and environmental performance strongly depend on regional ambient conditions and energy supply but neglect regional CO2 storage potentials. To assess DACCS performance holistically, a detailed global analysis is needed that accounts for the interplay of regional ambient conditions, energy supply, and CO2 storage potential. Hence, we present a novel method for the optimal siting of DACCS plants derived from optimising a dynamic process model that uses global hourly weather data and regionalised data on electricity supply and CO2 storage potential. The carbon removal rate (CRR) measures the climate benefit and describes the speed at which a DACCS plant generates net negative emissions. First, we assume that CO2 storage is possible everywhere. For four electricity supply scenarios, we show that the optimal siting of DACCS significantly increases the CRR when comparing the best and worst locations in each scenario: For a DACCS plant with a nameplate capture capacity of 4 kt CO2 y−1, the CRR can be increased by 63% from 2.16 to 3.53 kt CO2 y‑1 when using photovoltaic, and by 39% from 2.95 to 4.1 kt CO2 y‑1 when using wind power. Assuming a carbon-free electricity supply, the CRR varies between 3.17 and 4.17 kt CO2 y‑1 (32%). Second, we significantly narrow down optimal locations for DACCS considering regional CO2 storage potential through CO2 mineralisation. Overall, accounting for the interplay of regional DAC performance, energy supply, and CO2 storage potential can significantly improve DACCS siting.

Design Insights for Industrial CO2 Capture, Transport, and Storage Systems.

We present methods and insights for the design of CO2 capture, transport, and storage systems for industrial facilities with a case study focus on Louisiana. Our analytical framework includes (1) evaluating the scale and concentration of capturable CO2 emissions at individual facilities for the purpose of estimating the cost of CO2 capture retrofits that utilize various energy supply sources to meet parasitic demands; (2) screening to identify potential CO2 storage sites and estimate their capacities, injectivities, and costs; and (3) designing cost-minimized trucking or pipeline infrastructure connecting CO2 capture plants with storage sites, considering existing land uses, demographics, and a variety of social and environmental justice factors. Estimated levelized costs of capture at Louisiana's 190 industrial facilities range from below $50/tCO2 to above $500/tCO2, depending on facility-specific features. We identified 98 potential storage sites with storage costs ranging from $8 to $17/tCO2. We find that in most situations, pipelines are the least-costly mode of CO2 transport. When industrial facilities in a region share pipelines, aggregate pipeline mileage and average transport costs are dramatically lower than without sharing. Shared pipeline networks designed to avoid disadvantaged communities require right-of-way areas compared to those for networks that transect such communities, but result in 25% higher average per-tonne transport cost.

Learnings from up-scaling CO2 capture: Challenges and experiences with pilot work

Large-scale reduction of greenhouse gasses is necessary to abate the climate crisis, for which technological development will pave the way. Advancing research from academia to industry is crucial for technological progress. Pilot scale studies are the intermediate step from bench to industry. New challenges arise during technology upscaling, many of which have not been previously described in the literature. This article provides a guide of the do's and don'ts to researchers venturing into pilot work. The learnings and experiences gained by working with pilot-scale CO2 capture plant are presented and discussed. This work outlines the process challenges encountered during the commissioning and operation of a CO2 capture pilot using 30 wt% MEA and with a capacity to capture one tonne CO2/day from flue gas. Conducting pilot work in industrial facilities and outside controlled lab environments comes with unexpected problems. The issues faced during our pilot work are presented along with troubleshooting strategies and possible solutions. This work can aid researchers in identifying and rectifying operational challenges and conducting successful pilot campaigns.

A Low-Carbon Economic Dispatch Method for Power Systems With Carbon Capture Plants Based on Safe Reinforcement Learning

A Low-Carbon Economic Dispatch Method for Power Systems With Carbon Capture Plants Based on Safe Reinforcement Learning

Monoethanolamine Degradation Rates in Post-combustion CO2 Capture Plants with the Capture of 100% of the Added CO2

Monoethanolamine Degradation Rates in Post-combustion CO₂ Capture Plants with the Capture of 100% of the Added CO₂

Vapor absorption, compression and cascade heat pumps for carbon capture plants: Multi-criteria analysis and techno-economic assessment with different working fluids

Post-combustion CO2 capture plants include high costs that are associated with intense energy consumption. Heat pumps can recover heat from the flue gas and other sources in the CO2 emitting plant and upgrade it to reduce the use of fossil-based steam. The few studies regarding heat pumps in CO2 capture systems include arbitrary selection among limited working fluids, without optimizing the underlying cycle. This work implements a multi-criteria investigation of the performance of 20 working fluids for vapor compression heat pumps (VCHP) and 21 refrigerant/absorber pairs for absorption heat transformers (AHT), considering the operating optimization of each cycle for each fluid. A cascade cycle combining the two configurations (AHT-VCHP) is proposed, using the optimum fluids cyclopentane and water/potassium nitrate identified for the VCHP and the AHT. The annual operating costs are higher than the annualized capital expenditures. The proposed fluids outperform the conventional options. The VCHP with cyclopentane covers 58 % (2.68 GJ/t CO2) of the reboiler duty with recovered heat and exhibits the lowest cost of $33.8/tCO2. This is $18.7/tCO2 lower than the cost of the conventional direct contact cooling unit that is used in capture plants. The VCHP remains competitive for up to 57 % increase in the electricity price.

Techno-economic assessment of solar photovoltaic electrification and calcium looping technology as decarbonisation pathways of alumina industry

Aluminium industry stands out as a significant source of CO2 emissions, due partially to the high energy demand of the alumina extraction stage. Accordingly, this study explores the implementation of direct resistive heating of mid-temperature processes in alumina production as an alternative to decrease CO2 emissions. Additionally, two different strategies are evaluated to decarbonize alumina industry: the generation of renewable electricity through solar photovoltaic panels and the integration of a CO2 capture plant based on calcium looping technology. This work comprehends the modelling and sizing of these plants and the assessment of their economic performance through the calculation of their Net Present Value and their respective payback periods. The integration of both strategies into an alumina refinery model reveals that electrification of low and mid-temperature processes yields a 15 % reduction in CO2 direct emissions, whereas calcium looping demonstrates the potential to capture 97 % of emissions with a 7 % energy penalty. Also, economic assessments indicate substantial potential for improvement through in-site electricity generation via solar photovoltaic panels, exhibiting a payback time of 4.5 years. Conversely, the feasibility of a calcium-looping plant is hindered by high capital expenses, necessitating a longer payback period of 19–24 years. Sensitivity analyses underscore the suitability of in-site renewable electricity generation, whereas carbon emission taxes emerge as crucial in incentivizing carbon-neutral processes, with thresholds around 95–125 €/tonne of CO2. Despite potential deviations from real industrial settings, this study provides evidence for environmentally friendly strategies in alumina production that demonstrate limited adverse effects on economic performance.

Subsurface maturation in a saline aquifer CCS project development. Experience from the Northern Lights project, offshore Norway

The Northern Lights full-scale commercial CO 2 transport and storage project is open to multi-industry capture plants, in a business still under development. The geological sequestration concept of a sloping saline aquifer raised several subsurface challenges related to timeline, data availability and development constrains, which all affected the decision process. The subsurface focus on storage security required the different disciplines to perform adjustments to the established workflows available from the hydrocarbon industry. A risk management-focused approach, front-end loading and early emphasis on containment were paramount. Here we share the experience from the Northern Lights subsurface maturation process and highlight the crucial contribution from the different disciplines in a challenging new industry.

Hybrid Advanced Control Strategy for Post-Combustion Carbon Capture Plant by Integrating PI and Model-Based Approaches

Even though the energy penalties and solvent regeneration costs associated with amine-based absorption/stripping systems are important challenges, this technology remains highly recommended for post-combustion decarbonization systems given its proven capture efficacy and technical maturity. This study introduces a novel centralized and decentralized hybrid control strategy for the post-combustion carbon capture plant, aimed at mitigating main disturbances and sustaining high system performance. The strategy is rooted in a comprehensive mathematical model encompassing absorption and desorption columns, heat exchangers and a buffer tank, ensuring smooth operation and energy efficiency. The buffer tank is equipped with three control loops to finely regulate absorber inlet solvent solution parameters, preventing disturbance recirculation from the desorber. Additionally, a model-based controller, utilizing the model predictive control (MPC) algorithm, maintains a carbon capture yield of 90% and stabilizes the reboiler liquid temperature at 394.5 K by manipulating the influent flue gas to the lean solvent flowrates ratio and the heat duty of the reboiler. The hybrid MPC approach reveals efficiency in simultaneously managing targeted variables and handling complex input–output interactions. It consistently maintains the controlled variables at desired setpoints despite CO2 flue gas flow disturbances, achieving reduced settling time and low overshoot results. The hybrid control strategy, benefitting from the constraint handling ability of MPC, succeeds in keeping the carbon capture yield above the preset minimum value of 86% at all times, while the energy performance index remains below the favorable value of 3.1 MJ/kgCO2.

Demonstration of CO2 Capture Process Monitoring and Solvent Degradation Detection by Chemometrics at the Technology Centre Mongstad CO2 Capture Plant: Part II

Demonstration of CO₂ Capture Process Monitoring and Solvent Degradation Detection by Chemometrics at the Technology Centre Mongstad CO₂ Capture Plant: Part II

Multi-objective optimization of a hybrid carbon capture plant combining a Vacuum Pressure Swing Adsorption (VPSA) process with a Carbon Purification unit (CPU)

The imperative challenge posed by climate change requires urgent actions to counteract the harmful effects of greenhouse gas emissions, particularly CO2, which contributes to approximately 80 % of emissions responsible for global warming. A hybrid system combining Vacuum Pressure Swing Adsorption (VPSA) unit with a Cryogenic Carbon Purification Unit (CPU) is evaluated to enhance recovery and purity of CO2captured from flue gas containing CO2concentration ranging from 5 % to 20 %. VPSA preconcentrates the CO2and CPU completes the separation and purifies the CO2. The study uses surrogate models for multi-objective optimization, considering energy consumption, cost, and CO2recovery, providing a time-efficient approach for investigating computationally demanding processes. Results from the study indicate that the hybrid system achieves over 90 % recovery for flue gas concentration range considered, while ensuring the production of high-purity CO2(>99.99 %) suitable for transportation. A trade-off analysis reveals the balance between recovery, electricity consumption, and economic viability. A sensitivity analysis identifies parameters influencing recovery and energy consumption, providing guidance for future optimization efforts. The techno-economic analysis highlights the impact of electricity prices and carbon taxes on total costs, identifying an optimum towards higher recovery values under rising carbon taxes. Furthermore, the research underscores concentration-dependent economic feasibility, emphasizing the attractiveness of concentrations above 10 % compared with other technologies, which require higher concentrations. For an electricity price of 75 €.MWh−1, the total cost of the CO2 capture hydride system considering CO2 emissions with carbon tax of 100 €.tCO2−1 for concentrations ranging from 10 % to 20 % is from 123 to 80 €.tCO2−1, respectively. The analysis of the electricity source shows the importance of a low-carbon emission energy mix for optimal carbon emission reduction.

Carbon Neutrality Computational Cost Optimization for Economic Dispatch With Carbon Capture Power Plants in Smart Grid

To achieve carbon neutrality, reducing carbon emissions is crucial in dispatching problems in smart grid. Though renewable energy such as wind power has low carbon emissions, it suffers from random generation, which makes the thermal power necessary for a stable supply power system. To reduce carbon emissions, the thermal power plants are transformed into carbon capture power plants, which brings new challenges to economic dispatch algorithms. Besides, there are usually many constraints to keep the security operation of power systems, which incurs a large problem scale and high computational cost. Most existing methods either do not consider reducing carbon emissions, or suffer from high computational costs. In this paper, a framework for the carbon capture plants with wind power to reduce both running costs and carbon emissions is designed to support carbon neutrality. To reduce computational cost, initial-training and fine-tuning are used. A deep neural network is employed to describe the relationship between users' load and the constraints, which provides guides for finding the active constraints. Therefore, the problem scale can be significantly decreased, making the optimal dispatching strategy obtained quickly. The experimental results on real-world data show that the proposed framework can obtain the optimal strategy efficiently.

Optimization of CO2 capture plants with surrogate model uncertainties

CO2 capture plants can help reduce the cost of deploying capture systems across the globe. However, the CO2 variability and model uncertainty represent operational challenges to capture CO2 from different sources. This work proposes a framework for analyzing the optimal plant design considering different flue gas sources. We show a methodology to generate large data sets from optimization runs using rigorous models in Aspen Plus®. The efficiency of the approach allows its application to large-scale optimization problems, with an average CPU time per run of 176 s.We additionally build surrogate models (SMs) for the capital and operating costs of the capture plants, employing an iterative procedure to generate SMs using ALAMO. We systematically reject SMs with high uncertainty in the estimated parameters. This approach results in SMs with favorable bias-variance tradeoffs, enabling their effective application to optimization problems under uncertainty, as demonstrated with a pooling problem of CO2 streams.

Pilot-scale CO2 capture demonstration of heat integration through split flow configuration using 30 wt[formula omitted] MEA at a Waste-to-Energy facility

Post-combustion carbon capture is a well-established technology to limit CO2 from industrial emissions. However, challenges such as high energy requirements for solvent regeneration persist. Research is still being conducted to improve the energy efficiency of the process. This study aims to present the results of process intensification in a pilot-scale CO2 capture plant. The pilot-scale investigations were carried out at Amager Bakke, a Waste-to-Energy facility in Copenhagen, Denmark. The tests were conducted by implementing the split flow configuration as a method of process intensification. The CO2 rich stream was split before flowing through the rich/lean heat exchanger. One of the split streams was fed to the stripper top, and the other was sent through the heat exchanger and then fed to the stripper. The cold split stream at the stripper top is heated by the overhead vapours which would otherwise escape the stripper. The current work discusses the split flow configuration results of the pilot plant by employing 30wt% MEA as the solvent and compares it to the base-case performance of the pilot. Experiments were conducted by splitting 22% of the rich solvent and feeding it to the stripper top. The performance of the split flow configuration was analysed at different reboiler duties. A minimum specific reboiler duty of 3.45 GJ/tonne CO2 is obtained for the split flow configuration, which is less than the base case by 7.8%. Additionally, a reduction in the condenser duty by at least 68% was achieved by the implementation of the split flow configuration.

Ambient wind conditions impact on energy requirements of an offshore direct air capture plant

This study proposes an off-grid direct air (carbon) capture (DAC) plant installed on the deck of an offshore floating wind turbine. The main objective is to understand detailed flow characteristics and CO2 dispersion around air contactors when placed in close proximity to one another. A solid sorbent DAC design is implemented using a commercially deployed air contactor configuration and sorbent. The sorbent is assumed to undergo a temperature vacuum swing adsorption cycle. Computational fluid dynamics (CFD) is used to determine the local conditions entering each unit based on varying wind speed and angle. Two-dimensional (2D) simulations were used to determine the pressure drop through a detailed air contactor design considering the sorbents APDES-NFC, Tri-PE-MCM, MIL-101(Cr)-PEI-800, and Lewatit VP OC 106. Only APDES-NFC was explored for further analysis in the three dimensional (3D) CFD dispersion model. 3D simulations were used to model flow patterns and CO2 dispersion using passive scalars. A worst case scenario is analyzed for all DAC units in adsorption mode with fans running simultaneously. 2D simulations show an under utilization of contactor length, and quantify pressure loss curves for four common sorbents. One commercially deployed sorbent is considered for further analysis; a pressure drop of 390.62 Pa is experienced for a flow velocity of 0.73 m s−1 through a 1.5m×1.5m×1.5m contactor. Using 3D simulations, fan energy demands are computed based on flow velocities and applied pressure gradients. There is found to be a decrease in overall fan power demand as wind speed increases. High wind speeds can passively drive the adsorption process with fans shut off at certain wind directions. This occurs at an average contactor inlet velocity of 17.5 m s−1, correlating to a hub height (150 m) wind speed of 24 m s−1. Thermal energy demands are computed based on inlet CO2 concentrations entering downstream units. Thermodynamic work for desorption based on an assumed second law efficiency is compared to thermal energy for desorption computed using a simplified isotherm method, taking into account the impacts of humidity on CO2 adsorption. Going from 414.72 ppm to 300 ppm CO2 inlet concentration requires an additional 63.9 kWh (t- CO2)−1 using the 2nd law thermodynamic efficiency method and 25.9 kWh (t- CO2)−1 using the isotherm method. Contactor arrangement, wind angles, and wind speeds have a significant impact on flow patterns experienced, and resulting CO2 dispersion. High wind speeds assist in CO2 dispersion, resulting in higher inlet concentrations to downstream DAC units and decreased thermal energy requirement.

An optimal sustainable planning strategy for national carbon capture deployment: A review on the state of CO2 capture in Canada

AbstractThis study reviews the steps Canada is taking to address sustainable decarbonization in the context of carbon capture. This work also presents a new optimal framework for national optimal deployment in need of strategic carbon capture implementation. This framework considers external environmental and social considerations often missing from implementation frameworks, which will aid policy makers in more well‐rounded deployment decisions. Thus far, Canada's carbon projects have captured a total of 36.3 Mt of CO2 which has cost over $2.7 billion to implement. The Canadian case study utilizing the proposed optimal planning strategy shows that implementation of 58 post‐combustion carbon capture (PCC) plants located in seven provinces (Alberta, British Columbia, New Brunswick, Nova Scotia, Ontario, Quebec, and Saskatchewan) would result in Canada meeting the national targets. This implementation includes 16 plants removing emissions from the Electricity sector, 16 from the Heavy Industry sector, and 26 from the Oil and gas sector resulting in new emissions levels of 11.82 MtCO2, 27.63 MtCO2, and 107.01 MtCO2 in each sector, respectively. Additional case studies examined the impact of Alberta's emissions and varying the national targets resulting in different optimal implementations plans. Through a sensitivity analysis on these targets, it was determined that plant distribution is heavily dependent on provincial energy and CO2 transport prices. Additionally, if Alberta were to reduce their GHG emissions by 50% through alternative sustainable methods, only 35 PCC plants would be required to meet national targets. This framework provides a sustainable tool for decision‐makers to accelerate decarbonization.

Solar-Assisted Carbon Capture Process Integrated with a Natural Gas Combined Cycle (NGCC) Power Plant—A Simulation-Based Study

In the realm of Natural Gas Combined Cycle (NGCC) power plants, it is crucial to prioritize the mitigation of CO2 emissions to ensure environmental sustainability. The integration of post-combustion carbon capture technologies plays a pivotal role in mitigating greenhouse gas emissions enhancing the NGCC’s environmental profile by minimizing its carbon footprint. This research paper presents a comprehensive investigation into the integration of solar thermal energy into the Besmaya Natural Gas Combined Cycle (NGCC) power plant, located in Baghdad, Iraq. Leveraging advanced process simulation and modeling techniques employing Aspen Plus software, the study aims to evaluate the performance and feasibility of augmenting the existing NGCC facility with solar assistance for post-carbon capture. The primary objective of this research is to conduct a thorough simulation of the Besmaya NGCC power plant under its current operational conditions, thereby establishing a baseline for subsequent analyses. Subsequently, a solar-assisted post-combustion capture (PCC) plant is simulated and seamlessly integrated into the existing power infrastructure. To accurately estimate solar thermal power potential at the Baghdad coordinates, the System Advisor Model (SAM) is employed. The integration of solar thermal energy into the NGCC power plant is meticulously examined, and the resulting hybrid system’s technical viability and performance metrics are rigorously evaluated. The paper contributes to the field by providing valuable insights into the technical feasibility and potential benefits of incorporating solar thermal energy into conventional natural gas power generation infrastructure, particularly in the context of the Besmaya NGCC plant in Baghdad. The power generation capacity of the plant was set at 750 MW. With this capacity, the annual CO2 generation was estimated at 2,119,318 tonnes/year which was reduced to 18,064 tonnes/year (a 99% reduction). The findings aim to inform future decisions in the pursuit of sustainable and efficient energy solutions, addressing both environmental concerns and energy security in the region.

Measurement report: Method for evaluating CO2 emissions from a cement plant using atmospheric δ(O2 ∕ N2) and CO2 measurements and its implication for future detection of CO2 capture signals

Abstract. Continuous observations of atmospheric δ(O2/N2) and CO2 amount fractions have been carried out at Ryori (RYO), Japan, since August 2017. In these observations, the O2 : CO2 exchange ratio (ER, -Δy(O2)Δy(CO2)-1) has frequently been lower than expected from short-term variations in emissions from terrestrial biospheric activities and combustion of liquid, gas, and solid fuels. This finding suggests a substantial effect of CO2 emissions from a cement plant located about 6 km northwest of RYO. To evaluate this effect quantitatively, we simulated CO2 amount fractions in the area around RYO by using a fine-scale atmospheric transport model that incorporated CO2 fluxes from terrestrial biospheric activities, fossil fuel combustion, and cement production. The simulated CO2 amount fractions were converted to O2 amount fractions by using the respective ER values of 1.1, 1.4, and 0 for the terrestrial biospheric activities, fossil fuel combustion, and cement production. Thus obtained O2 and CO2 amount fraction changes were used to derive a simulated ER for comparison with the observed ER. To extract the contribution of CO2 emissions from the cement plant, we used y(CO2∗) as an indicator variable, where y(CO2∗) is a conservative variable for terrestrial biospheric activities and fossil fuel combustion obtained by simultaneous analysis of observed δ(O2/N2) and CO2 amount fractions and simulated ERs. We confirmed that the observed and simulated ER values and also the y(CO2∗) values and simulated CO2 amount fractions due only to cement production were generally consistent. These results suggest that combined measurements of δ(O2/N2) and CO2 amount fractions will be useful for evaluating CO2 capture from flue gas at carbon capture and storage (CCS) plants, which, similar to a cement plant, change CO2 amount fractions without changing O2 values, although CCS plants differ from cement plants in the direction of CO2 exchange with the atmosphere.